The impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan

Abstract

Assessing the impact of temperature on COVID-19 epidemiology is critical for implementing non-pharmaceutical interventions. However, few studies have accounted for the nature of contagious diseases, i.e., their dependent happenings. We aimed to quantify the impact of temperature on the transmissibility and virulence of COVID-19 in Tokyo, Japan, employing two epidemiological measurements of transmissibility and severity: the effective reproduction number ($$R_{t}$$ R t ) and case fatality risk (CFR). We estimated the $$R_{t}$$ R t and time-delay adjusted CFR and to subsequently assess the nonlinear and delayed effect of temperature on $$R_{t}$$ R t and time-delay adjusted CFR. For $$R_{t}$$ R t at low temperatures, the cumulative relative risk (RR) at the first temperature percentile (3.3 °C) was 1.3 (95% confidence interval (CI): 1.1–1.7). As for the virulence to humans, moderate cold temperatures were associated with higher CFR, and CFR also increased as the temperature rose. The cumulative RR at the 10th and 99th percentiles of temperature (5.8 °C and 30.8 °C) for CFR were 3.5 (95% CI: 1.3–10.0) and 6.4 (95% CI: 4.1–10.1). Our results suggest the importance to take precautions to avoid infection in both cold and warm seasons to avoid severe cases of COVID-19. The results and our proposed approach will also help in assessing the possible seasonal course of COVID-19 in the future.

The Relative Roles of Ambient Temperature and Mobility Patterns in Shaping the Transmission Heterogeneity of SARS-CoV-2 in Japan

“…Another significant study in the USA investigated the attributable fractions of the mean ambient temperature at 3.7% [ 44 ]. Similarly, a study in Tokyo also reported a cumulative RR of 1.3 (95% CI: 1.1–1.7) at the first mean ambient temperature percentile (i.e., 3.3 °C), which provides empirical support for our findings [ 48 ]. Furthermore, the laboratory studies conducted by Chin et al suggested that SARS-CoV-2 is stable at approximately 4.0 °C, but at approximately 70.0 °C (higher temperature), the virus inactivation time decreases to five minutes, supporting existing epidemiological findings [ 68 ].…”

“…Time-series statistical regression modeling, which is often used to quantify short-term associations of environmental exposures with health outcomes, such as infectious diseases, would allow investigators to control for seasonality, long-term trends, other confounders, and autocorrelation, as well as explore the association of delayed exposure effects [ 46 ]. Despite the advantages of this method, its use has not been overly prevalent in research [ 13 , 44 , 45 , 47 , 48 ]. Additionally, many of these previous studies did not control for or only controlled for a few crucial potential confounders when describing the disease transmission dynamics, which include other behavioral and environmental drivers, the implementation of non-pharmaceutical interventions (NPIs), and temporal changes in population immunity.…”

“…The transmission heterogeneity of infectious diseases should also be considered when formulating time-series statistical regression models. Generally, the observation of each case with a contagious disease is not independent, and this characteristic is referred to as a dependent happening [ 48 ]. In particular, the phenomenon of super-spreading SARS-CoV-2 events, owing to the heterogeneity (i.e., overdispersion) in the number of secondary transmissions, is a crucial epidemiological characteristic that leads to sustained transmission.…”

Mass mortality associated with Carpione Rhabdovirus in golden pompano (Trachinotus ovatus) in China: First report

“…The pathogenicity of rhabdoviruses is usually influenced by temperature, host species, and age [31,32]. Boovo et al [28] demonstrated that high mortality in Carpio fry exposed to CAPRV at 10 or 15°C.…”

Role of meteorological factors on SARS-CoV-2 infection incidence in Italy and Spain before the vaccination campaign. A multi-city time series study

“…On reporting the results, we expressed the effects of meteorological variables relative to a reference level set to be the median of observation distribution. We obtained associations curves representing how the relative risk change by different levels of the meteorological variables, which can be compared with the results of previous studies ( Guo et al, 2021 ; Nottmeyer and Sera, 2021 ; Yamasaki et al, 2021 ; Zhu et al, 2021 ). This representation understates uncertainty in the reference level and overstates uncertainty in other exposure levels.…”